cnns-based methods [1]} have recently demonstrated their superiority compared to the approaches relying on hand-crafted features. However, despite the recent advances, the pedestrian detection task remains a challenging active research area in Computer Vision. One of the main issues still affecting cnn-based approaches for pedestrian detection is related to data scarcity. Indeed, the crux of cnns is that to generalize well at inference time, they require a massive amount of diverse well-labeled data during the training phase, covering the widest number of different scenarios.
While there exist some large annotated generic datasets suitable for training these supervised learning networks, such as ImageNet [2]} and MS COCO [3]}, in many real-world situations they are not enough. Hence, as a consequence, a model that learned from these data usually experiences a drastic drop in performance at inference time when applied to other scenarios never seen during the training phase.
This performance gap is due to the fact that the network learns from data belonging to one domain (named the training or source domain) and is then applied on another different domain (the test or target domain), and is commonly referred as Domain Shift [4]}.
